data <- read.csv("../week1/201407-citibike-tripdata.csv")
# replace spaces in column names with underscores
names(data) <- gsub(' ', '_', names(data))
stations <- data %>% select(start.station.name,start.station.longitude,start.station.latitude) %>% unique() 
names(stations) <- c("name", "longitude", "latitude")

Make a map showing the location of each Citibike station using ggmap

nyc_map <- get_map(location = c(lon = -74.00, lat = 40.71), maptype = "terrain", zoom = 12)
## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=40.71,-74&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
ggmap(nyc_map) +
  geom_point(data = stations, aes(x=longitude, y=latitude))

Do the same using leaflet, adding a popup that shows the name of the station when it’s clicked on

leaflet() %>%
  addTiles() %>%
  setView(-74.00, 40.71, zoom = 12) %>%
  addCircleMarkers(lng = stations$longitude, lat=stations$latitude, popup = stations$name, radius = 1) %>%
  addProviderTiles("CartoDB.Positron")

Then do a spatial join to combine this data frame with the Pediacities NYC neighborhood shapefile data

stations_spdf <- stations %>% filter(longitude, latitude)
r <- GET('http://data.beta.nyc//dataset/0ff93d2d-90ba-457c-9f7e-39e47bf2ac5f/resource/35dd04fb-81b3-479b-a074-a27a37888ce7/download/d085e2f8d0b54d4590b1e7d1f35594c1pediacitiesnycneighborhoods.geojson')
nyc_neighborhoods_spdf <- readOGR(content(r,'text'), 'OGRGeoJSON', verbose = F)
## No encoding supplied: defaulting to UTF-8.
coordinates(stations_spdf) <- ~longitude + latitude
proj4string(stations_spdf) <- proj4string(nyc_neighborhoods_spdf)
matches <- over(stations_spdf, nyc_neighborhoods_spdf)
stations <- cbind(stations, matches)

Make a map showing the number of unique Citibike stations in each neighborhood First do this using ggmap where the fill color encodes the number of stations

neighborhood_num_stations <- stations %>% group_by(neighborhood) %>% summarize("num_stations" = n())
nyc_neighborhoods <- tidy(nyc_neighborhoods_spdf, region = "neighborhood") %>%
  left_join(., neighborhood_num_stations, by = c("id" = "neighborhood")) %>% filter(! is.na(num_stations))
## Warning: Column `id`/`neighborhood` joining character vector and factor,
## coercing into character vector
ggmap(nyc_map) +
  geom_polygon(data = nyc_neighborhoods, aes(x = long, y=lat, group = group, fill = num_stations), color = "blue")

Then do the same using leaflet, adding a popup that shows the number of stations in a neighborhood when its shape is clicked on

map_data_spdf <- geo_join(nyc_neighborhoods_spdf, neighborhood_num_stations, "neighborhood", "neighborhood")
pal <- colorNumeric(palette = "RdBu", domain = range(map_data_spdf@data$num_stations, na.rm = T))
leaflet(map_data_spdf) %>%
  addTiles() %>%
  addPolygons(fillColor = ~pal(num_stations), popup = ~paste(num_stations)) %>%
  addProviderTiles("CartoDB.Positron") %>%
  setView(-73.98, 40.75, zoom = 13)

Now create a new data frame that has the total number of trips that depart from each station at each hour of the day on July 14th

hourly_trips <- data %>% filter(as.Date(starttime) == '2014-07-14') %>% mutate(hour = hour(starttime))

Do a spatial join to combine this data frame with the Pediacities NYC neighborhood shapefile data

hourly_trips_spdf <- hourly_trips %>% filter(start.station.longitude, start.station.latitude)
coordinates(hourly_trips_spdf) <- ~start.station.longitude + start.station.latitude
proj4string(hourly_trips_spdf) <- proj4string(nyc_neighborhoods_spdf)
matches <- over(hourly_trips_spdf, nyc_neighborhoods_spdf)
hourly_trips <- cbind(hourly_trips, matches)

hourly_trips <- hourly_trips %>% group_by(hour, start.station.name, start.station.latitude, neighborhood, start.station.longitude) %>% summarize("count" = n()) %>% filter(hour == 9|hour ==13|hour==17|hour==22) %>% group_by(hour) %>%
  mutate(percentage = count/sum(count))

nyc_trips <- tidy(nyc_neighborhoods_spdf, region = "neighborhood") %>%
left_join(., hourly_trips, by = c("id" = "neighborhood")) %>% filter(! is.na(count))
## Warning: Column `id`/`neighborhood` joining character vector and factor,
## coercing into character vector

Make a ggmap plot showing the number of trips that leave from each neighborhood at 9am, 1pm, 5pm, and 10pm, faceted by hour, where each facet contains a map where the fill color encodes the number of departing trips in each neighborhood

ggmap(nyc_map) +
geom_polygon(data = nyc_trips, aes(x = long, y=lat, group = group, fill = percentage), color = "blue") +
scale_fill_gradient(low = "blue", high = "red")+
facet_wrap(~hour)